The present invention relates to discovering anomalous, or unusual, patterns of motion activities in visual image data.
Determining and recognizing anomalous patterns of motion activities in visual image data is useful in determining occurrences or absences of certain activities or events. For example, image data of structures may be monitored for changes in expected or normal visual data patterns that are indicative of events and behaviors diverging from norms (for example, immediate or potential failures of structural components, or human activities outside of compliance with usual safety or other activity processes and policies). If readily distinctive to human analysis, such anomalous patterns of motion may be identified by capturing and recording visual data through still image and video systems for subsequent or contemporaneous analysis. However, human review and analysis of visual data may be difficult, time consuming or inefficient, and even non-feasible with large amounts of data. Some anomalous patterns may be subtle, or even discoverable only through computerized analysis of the visual data.
Automated video systems and methods are known wherein computers or other programmable devices directly analyze video data and attempt to recognize objects, people, events or activities of concern, etc., through identifying anomalous motion patterns through computer vision applications. However, discernment of anomalous motion patterns from normal patterns, events, etc., by automated video surveillance systems and methods systems is often not reliable in realistic, real-world environments and applications due to a variety of factors. For example, visual image data may be difficult to analyze or vary over time due to clutter, poor or variable lighting and object resolutions, distracting competing visual information, etc. False alerts or missed event recognitions must also occur at an acceptable level.